Frequency Effect on Word Choice
Main Model
In our main analysis, we considered participants’ likelihood of choosing the word for the nearest compass direction, dependent on whether that compass direction was a high- or a low-frequency word, while controlling for the distance from the nearest learned compass direction. We focused specifically on low-frequency/high-frequency trials, in which a compass direction was tested in between a low-frequency and a high-frequency trained direction.
As a conservative test, we retained only trials in which participants chose one of the two principal direction words within 45° of the stimulus direction (94.41% of all low-frequency/high-frequency trials).
#just trials with a left or right angle choice
#model
m <- glmer(matchChoice~hfTrial+angleDiffFromMatchC+(1+hfTrial+angleDiffFromMatchC|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: matchChoice ~ hfTrial + angleDiffFromMatchC + (1 + hfTrial +
## angleDiffFromMatchC | subjCode) + (1 | targetLabel)
## Data: subset(d, listChoice == 1)
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 2453.4 2514.6 -1216.7 2433.4 3348
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.9254 0.0982 0.2040 0.4370 2.2482
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjCode (Intercept) 0.240227 0.49013
## hfTrial 1.211711 1.10078 -0.26
## angleDiffFromMatchC 0.001383 0.03719 -0.98 0.06
## targetLabel (Intercept) 0.020018 0.14149
## Number of obs: 3358, groups: subjCode, 39; targetLabel, 18
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.34236 0.12216 19.174 < 2e-16 ***
## hfTrial 0.70564 0.20946 3.369 0.000755 ***
## angleDiffFromMatchC -0.22760 0.01347 -16.892 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) hfTril
## hfTrial -0.145
## anglDffFrMC -0.778 0.039
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
confint(m,method="Wald")[8:10,]
## 2.5 % 97.5 %
## (Intercept) 2.1029300 2.5817911
## hfTrial 0.2951182 1.1161689
## angleDiffFromMatchC -0.2540037 -0.2011891
#calculate shift in x-axis units (degrees of angle)
shift_x <- -(summary(m)$coefficients[1,1]+0.5*summary(m)$coefficients[2,1])/summary(m)$coefficients[3,1] + (summary(m)$coefficients[1,1]-0.5*summary(m)$coefficients[2,1])/summary(m)$coefficients[3,1]
#low 95% CI
shift_x_lower <- -(summary(m)$coefficients[1,1]+0.5*confint(m,method="Wald")[8:10,][2,1])/summary(m)$coefficients[3,1] + (summary(m)$coefficients[1,1]-0.5*confint(m,method="Wald")[8:10,][2,1])/summary(m)$coefficients[3,1]
#high 95% CI
shift_x_upper <- -(summary(m)$coefficients[1,1]+0.5*confint(m,method="Wald")[8:10,][2,2])/summary(m)$coefficients[3,1] + (summary(m)$coefficients[1,1]-0.5*confint(m,method="Wald")[8:10,][2,2])/summary(m)$coefficients[3,1]
## Main model has a singular fit warning - since this fit does not appear to impact fit, we retained the more complex random effects structure.
## However, we also fit a simplified model with the random slope for (the less theoretically important predictor) angleDiffFromMatchC removed, to ensure that the results are similar across different random effects structures and to alleviate concerns about a the boundary fit.
## This model yields very similar results (uncomment model below to view)
# m <- glmer(matchChoice~hfTrial+angleDiffFromMatchC+(1+hfTrial|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
# summary(m)
This effect corresponded to an estimated 3.1° shift (95% CI = [1.3°, 4.9°]) in participants’ decision boundary for high-frequency words as compared to low-frequency words.
Robustness Checks
Controlling for final retention accuracy of labels on each trial
To ensure that the frequency effect is not an artifact of participants’ being slightly more likely to forget the low-frequency labels, we first re-fit the model while controlling for participants’ accuracy during the Untimed Retention Test for the two (nearby) compass directions involved in each trial.
#controlling for accuracy for nearby labels
m <- glmer(matchChoice~hfTrial+angleDiffFromMatchC+finalAccuracyNearbyLabels+(1+hfTrial+angleDiffFromMatchC+finalAccuracyNearbyLabels|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## matchChoice ~ hfTrial + angleDiffFromMatchC + finalAccuracyNearbyLabels +
## (1 + hfTrial + angleDiffFromMatchC + finalAccuracyNearbyLabels |
## subjCode) + (1 | targetLabel)
## Data: subset(d, listChoice == 1)
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 2461.2 2553.0 -1215.6 2431.2 3343
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -9.3204 0.0982 0.2044 0.4364 2.2383
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjCode (Intercept) 0.210633 0.45895
## hfTrial 1.219664 1.10438 -0.83
## angleDiffFromMatchC 0.001256 0.03544 0.49 0.08
## finalAccuracyNearbyLabels 0.637278 0.79830 -0.81 0.35 -0.91
## targetLabel (Intercept) 0.017727 0.13314
## Number of obs: 3358, groups: subjCode, 39; targetLabel, 18
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 1.97635 0.94416 2.093 0.036329 *
## hfTrial 0.71757 0.21023 3.413 0.000642 ***
## angleDiffFromMatchC -0.22721 0.01337 -16.997 < 2e-16 ***
## finalAccuracyNearbyLabels 0.36827 0.95035 0.388 0.698380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) hfTril anDFMC
## hfTrial -0.054
## anglDffFrMC -0.054 0.047
## fnlAccrcyNL -0.991 0.038 -0.046
## optimizer (bobyqa) convergence code: 1 (bobyqa -- maximum number of function evaluations exceeded)
## boundary (singular) fit: see ?isSingular
confint(m,method="Wald")[12:15,]
## 2.5 % 97.5 %
## (Intercept) 0.1258202 3.8268763
## hfTrial 0.3055331 1.1296137
## angleDiffFromMatchC -0.2534079 -0.2010068
## finalAccuracyNearbyLabels -1.4943857 2.2309223
## maximal model yields a singular fit
## model with simpler random-effects structure (removing non-theoretically important random slope for angleDiffFromMatchC and finalAccuracyNearbyLabels) yields similar results for frequency effect without singular fit
# m <- glmer(matchChoice~hfTrial+angleDiffFromMatchC+finalAccuracyNearbyLabels+(1+hfTrial|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
# summary(m)
Including only participants with perfect recall for all compass directions at the end of the experiment
To further ensure that the frequency effect is not an artifact of participants’ being slightly more likely to forget the low-frequency labels, we next re-fit the same model using a stricter inclusion criterion, including only participants who recalled all items correctly during the Untimed Retetion test.
final_accuracy <- d %>%
filter(trialType=="finalName") %>%
group_by(subjCode,trialType) %>%
summarize(N=n(),accuracy=mean(isRight)) %>%
ungroup()
#select only participants with perfect recall on the final test block
perfect_final_accuracy_subjects <- as.character(filter(final_accuracy,accuracy==1)$subjCode)
m=glmer(matchChoice~hfTrial+angleDiffFromMatchC+(1+hfTrial+angleDiffFromMatchC|subjCode)+(1|targetLabel),data=subset(d, listChoice==1&subjCode %in% perfect_final_accuracy_subjects),family=binomial,glmerControl(optimizer="bobyqa"))
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: matchChoice ~ hfTrial + angleDiffFromMatchC + (1 + hfTrial +
## angleDiffFromMatchC | subjCode) + (1 | targetLabel)
## Data:
## subset(d, listChoice == 1 & subjCode %in% perfect_final_accuracy_subjects)
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 2108.7 2168.6 -1044.3 2088.7 2946
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.7064 0.0947 0.1974 0.4321 2.1530
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjCode (Intercept) 0.187489 0.43300
## hfTrial 0.785634 0.88636 -0.17
## angleDiffFromMatchC 0.001241 0.03523 -0.96 -0.11
## targetLabel (Intercept) 0.074566 0.27307
## Number of obs: 2956, groups: subjCode, 34; targetLabel, 18
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.46765 0.13898 17.755 < 2e-16 ***
## hfTrial 0.54033 0.19591 2.758 0.00582 **
## angleDiffFromMatchC -0.23838 0.01465 -16.268 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) hfTril
## hfTrial -0.085
## anglDffFrMC -0.722 -0.001
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
confint(m,method="Wald")[8:10,]
## 2.5 % 97.5 %
## (Intercept) 2.1952530 2.7400423
## hfTrial 0.1563494 0.9243204
## angleDiffFromMatchC -0.2671023 -0.2096603
## maximal model yields a singular fit
## model with simpler random-effects structure (random slope for angleDiffFromMatchC removed) yields similar results without singular fit
# m=glmer(matchChoice~hfTrial+angleDiffFromMatchC+(1+hfTrial|subjCode)+(1|targetLabel),data=subset(d, listChoice==1&subjCode %in% perfect_final_accuracy_subjects),family=binomial,glmerControl(optimizer="bobyqa"))
# summary(m)
Controlling for compass direction character length
The words given to each of the compass directions varied in character length (and therefore perhaps in how easy they are to produce/ type). Beyond randomly assigning compass directions and counterbalancing their roles across participants, we also fit all models with by-item random effects to ensure that the effect of frequency generalizes across items. In the following model, we also explicitly control for character length to ensure that the effects hold even after accounting for character length of the nearest/ target compass direction.
m <- glmer(matchChoice~hfTrial+angleDiffFromMatchC+nearestLabel_length+(1+hfTrial+angleDiffFromMatchC+nearestLabel_length|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: matchChoice ~ hfTrial + angleDiffFromMatchC + nearestLabel_length +
## (1 + hfTrial + angleDiffFromMatchC + nearestLabel_length |
## subjCode) + (1 | targetLabel)
## Data: subset(d, listChoice == 1)
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 2444.1 2535.9 -1207.1 2414.1 3343
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.0225 0.0931 0.2010 0.4242 2.3324
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjCode (Intercept) 2.899e+00 1.703e+00
## hfTrial 1.359e+00 1.166e+00 -0.04
## angleDiffFromMatchC 1.458e-03 3.819e-02 0.22 0.00
## nearestLabel_length 1.869e-01 4.323e-01 -0.96 -0.03 -0.46
## targetLabel (Intercept) 8.176e-09 9.042e-05
## Number of obs: 3358, groups: subjCode, 39; targetLabel, 18
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.57496 0.39227 6.564 5.23e-11 ***
## hfTrial 0.68557 0.21902 3.130 0.00175 **
## angleDiffFromMatchC -0.23066 0.01371 -16.829 < 2e-16 ***
## nearestLabel_length -0.04607 0.09452 -0.487 0.62595
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) hfTril anDFMC
## hfTrial -0.040
## anglDffFrMC -0.096 0.021
## nrstLbl_lng -0.955 -0.005 -0.144
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
confint(m,method="Wald")[8:10,]
## 2.5 % 97.5 %
## .sig08 NA NA
## .sig09 NA NA
## .sig10 NA NA
## maximal model yields a singular fit
## model with simpler random-effects structure (random slopes for angleDiffFromMatchC and nearestLabel_length removed) yields similar results without singular fit
# m <- glmer(matchChoice~hfTrial+angleDiffFromMatchC+nearestLabel_length+(1+hfTrial|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
# summary(m)
Controlling for number of training blocks
Since participants were required to produce all 8 compass directions perfectly during a Word Recall block in order to advance to the Treasure Hunt Phase (otherwise returning to the Word Learning Phase for further training), participants varied in the duration of their training. The extent to which participants were trained on the compass directions may influence the degree to which participants exhibited an effect of frequency on lexical selection. In the main logistic mixed-effects analyses demonstrating the effect of word frequency on lexical selection, we also fit a model controlling for differences in training duration by including the number of training blocks as a fixed effect.
m <- glmer(matchChoice~hfTrial+angleDiffFromMatchC+num_pairlearn_blocks+(1+hfTrial+angleDiffFromMatchC|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula: matchChoice ~ hfTrial + angleDiffFromMatchC + num_pairlearn_blocks +
## (1 + hfTrial + angleDiffFromMatchC | subjCode) + (1 | targetLabel)
## Data: subset(d, listChoice == 1)
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 2455.4 2522.7 -1216.7 2433.4 3347
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -8.9335 0.0982 0.2041 0.4372 2.2454
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjCode (Intercept) 0.239875 0.48977
## hfTrial 1.212121 1.10096 -0.26
## angleDiffFromMatchC 0.001383 0.03718 -0.98 0.06
## targetLabel (Intercept) 0.019839 0.14085
## Number of obs: 3358, groups: subjCode, 39; targetLabel, 18
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.356778 0.202841 11.619 < 2e-16 ***
## hfTrial 0.705767 0.209484 3.369 0.000754 ***
## angleDiffFromMatchC -0.227600 0.013474 -16.892 < 2e-16 ***
## num_pairlearn_blocks -0.003137 0.035225 -0.089 0.929045
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) hfTril anDFMC
## hfTrial -0.080
## anglDffFrMC -0.471 0.040
## nm_prlrn_bl -0.799 -0.008 0.004
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
confint(m,method="Wald")[8:11,]
## 2.5 % 97.5 %
## (Intercept) 1.95921785 2.75433905
## hfTrial 0.29518598 1.11634837
## angleDiffFromMatchC -0.25400799 -0.20119249
## num_pairlearn_blocks -0.07217659 0.06590322
## maximal model yields a singular fit
## model with simpler random-effects structure (random slope for angleDiffFromMatchC removed) yields similar results without singular fit
# m <- glmer(matchChoice~hfTrial+angleDiffFromMatchC+num_pairlearn_blocks+(1+hfTrial|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
# summary(m)
Checking for an interaction with distance
We also investigated whether distance interacted with frequency condition in predicting lexical selection. We find no evidence of an interaction between frequency and distance.
#full interaction model
#does not converge (boundary fit)
#m <- glmer(matchChoice~hfTrial*angleDiffFromMatchC+(1+hfTrial*angleDiffFromMatchC|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
#simplified random effects structure preserving critical interaction random effect (qualitatively similar results to more complex models)
m <- glmer(matchChoice~hfTrial*angleDiffFromMatchC+(1+hfTrial:angleDiffFromMatchC|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
summary(m)
## Generalized linear mixed model fit by maximum likelihood (Laplace
## Approximation) [glmerMod]
## Family: binomial ( logit )
## Formula:
## matchChoice ~ hfTrial * angleDiffFromMatchC + (1 + hfTrial:angleDiffFromMatchC |
## subjCode) + (1 | targetLabel)
## Data: subset(d, listChoice == 1)
## Control: glmerControl(optimizer = "bobyqa")
##
## AIC BIC logLik deviance df.resid
## 2477.5 2526.5 -1230.8 2461.5 3350
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -11.2560 0.0964 0.2240 0.4624 2.6402
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjCode (Intercept) 0.09213 0.3035
## hfTrial:angleDiffFromMatchC 0.01640 0.1280 -0.67
## targetLabel (Intercept) 0.02173 0.1474
## Number of obs: 3358, groups: subjCode, 39; targetLabel, 18
##
## Fixed effects:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 2.21327 0.09930 22.288 < 2e-16 ***
## hfTrial 0.84854 0.15822 5.363 8.19e-08 ***
## angleDiffFromMatchC -0.21592 0.01152 -18.735 < 2e-16 ***
## hfTrial:angleDiffFromMatchC -0.02118 0.03049 -0.694 0.487
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) hfTril anDFMC
## hfTrial -0.093
## anglDffFrMC -0.597 0.140
## hfTrl:nDFMC -0.132 -0.535 -0.125
Reaction Time Analysis
Descriptives
We investigated participants’ speed in responding on trials in which they chose the nearest word (thereby maximizing message alignment - analogous to RT on “correct” trials).
# splitting reaction time by word frequency on trials during the Treasure Hunt Phase
summarized_rt <- d %>%
filter(matchChoice==1) %>%
summarySEwithin(measurevar="rt",withinvars=c("hfTrial"),idvar="subjCode") %>%
mutate(lower_ci = rt - ci,
upper_ci = rt + ci) %>%
mutate(hfTrial=ifelse(hfTrial==-0.5,"low-frequency","high-frequency"))%>%
select(-sd,-ci,-rt_norm)
kable(summarized_rt)
| low-frequency |
831 |
2663.290 |
43.89452 |
2577.133 |
2749.448 |
| high-frequency |
1920 |
2208.123 |
24.63067 |
2159.817 |
2256.429 |
summarized_rt_block <- d %>%
filter(matchChoice==1) %>%
summarySEwithin(measurevar="rt",withinvars=c("block","hfTrial"),idvar="subjCode") %>%
mutate(lower_ci = rt - ci,
upper_ci = rt + ci) %>%
mutate(hfTrial=ifelse(hfTrial==-0.5,"low-frequency","high-frequency")) %>%
select(-sd,-ci,-rt_norm)
kable(summarized_rt_block)
| test_a |
low-frequency |
298 |
2697.804 |
60.73265 |
2578.283 |
2817.325 |
| test_a |
high-frequency |
1244 |
2171.240 |
24.96053 |
2122.271 |
2220.210 |
| test_b |
low-frequency |
533 |
2643.994 |
44.40167 |
2556.770 |
2731.218 |
| test_b |
high-frequency |
676 |
2275.995 |
33.75532 |
2209.717 |
2342.273 |
Linear mixed-effects model
We fit a linear mixed-effect model predicting participants’ reaction times from Word Frequency (centered; High = -0.5 vs. Low = -0.5) and Distance from Nearest Principal Direction with the same random effects structure as above.
#RT effect on trials in which nearest word is chosen
m=lmer(rt~hfTrial+angleDiffFromMatchC+(1+hfTrial+angleDiffFromMatchC|subjCode)+(1|targetLabel),data=subset(d, matchChoice==1), control=lmerControl(optimizer="bobyqa"))
summary(m)
## Linear mixed model fit by REML ['lmerMod']
## Formula:
## rt ~ hfTrial + angleDiffFromMatchC + (1 + hfTrial + angleDiffFromMatchC |
## subjCode) + (1 | targetLabel)
## Data: subset(d, matchChoice == 1)
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 44593.7
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5725 -0.6756 -0.2324 0.4728 6.2020
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjCode (Intercept) 103153.79 321.176
## hfTrial 93233.81 305.342 -0.68
## angleDiffFromMatchC 20.38 4.514 0.12 0.29
## targetLabel (Intercept) 24843.59 157.618
## Residual 611630.85 782.068
## Number of obs: 2751, groups: subjCode, 39; targetLabel, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2455.289 65.679 37.38
## hfTrial -405.370 61.330 -6.61
## angleDiffFromMatchC 10.071 2.665 3.78
##
## Correlation of Fixed Effects:
## (Intr) hfTril
## hfTrial -0.483
## anglDffFrMC 0.017 0.182
Anova(m,type="III",test="F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: rt
## F Df Df.res Pr(>F)
## (Intercept) 1396.774 1 47.493 < 2.2e-16 ***
## hfTrial 43.418 1 40.686 6.508e-08 ***
## angleDiffFromMatchC 14.193 1 36.902 0.0005763 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
confint(m,method="Wald")
## 2.5 % 97.5 %
## .sig01 NA NA
## .sig02 NA NA
## .sig03 NA NA
## .sig04 NA NA
## .sig05 NA NA
## .sig06 NA NA
## .sig07 NA NA
## .sigma NA NA
## (Intercept) 2326.560214 2584.01705
## hfTrial -525.574789 -285.16436
## angleDiffFromMatchC 4.848783 15.29387
#no interaction with block
#include highest order interaction terms as random slopes
m=lmer(rt~(hfTrial+angleDiffFromMatchC)*blockC+(1+hfTrial:blockC+angleDiffFromMatchC:blockC|subjCode)+(1|targetLabel),data=subset(d,matchChoice==1), control=lmerControl(optimizer="bobyqa"))
summary(m)
## Linear mixed model fit by REML ['lmerMod']
## Formula: rt ~ (hfTrial + angleDiffFromMatchC) * blockC + (1 + hfTrial:blockC +
## angleDiffFromMatchC:blockC | subjCode) + (1 | targetLabel)
## Data: subset(d, matchChoice == 1)
## Control: lmerControl(optimizer = "bobyqa")
##
## REML criterion at convergence: 44581.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3139 -0.6736 -0.2381 0.4750 6.0297
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## subjCode (Intercept) 127481 357.04
## hfTrial:blockC 50071 223.76 0.96
## blockC:angleDiffFromMatchC 1268 35.61 -0.74 -0.53
## targetLabel (Intercept) 27225 165.00
## Residual 621439 788.31
## Number of obs: 2751, groups: subjCode, 39; targetLabel, 18
##
## Fixed effects:
## Estimate Std. Error t value
## (Intercept) 2480.092 75.690 32.766
## hfTrial -410.046 37.331 -10.984
## angleDiffFromMatchC 19.388 4.838 4.007
## blockC -166.527 60.974 -2.731
## hfTrial:blockC 145.642 77.728 1.874
## angleDiffFromMatchC:blockC -8.396 11.276 -0.745
##
## Correlation of Fixed Effects:
## (Intr) hfTril anDFMC blockC hfTr:C
## hfTrial -0.074
## anglDffFrMC -0.064 0.003
## blockC 0.057 0.150 -0.824
## hfTrl:blckC 0.394 -0.085 -0.020 -0.180
## anglDfFMC:C -0.569 -0.016 0.104 -0.132 -0.124
## optimizer (bobyqa) convergence code: 0 (OK)
## boundary (singular) fit: see ?isSingular
Anova(m,type="III",test="F")
## Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df)
##
## Response: rt
## F Df Df.res Pr(>F)
## (Intercept) 1072.3726 1 51.04 < 2.2e-16 ***
## hfTrial 119.1075 1 2460.66 < 2.2e-16 ***
## angleDiffFromMatchC 15.9340 1 2690.76 6.734e-05 ***
## blockC 7.4101 1 2687.40 0.006528 **
## hfTrial:blockC 3.4931 1 48.34 0.067691 .
## angleDiffFromMatchC:blockC 0.5512 1 38.75 0.462302
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Plot
Plot the effect of training frequency on word/ compass direction choice.
#refit model without centering angle for simpler plotting (coefficients essentially equivalent)
m <- glmer(matchChoice~hfTrial+angleDiffFromMatch+(1+hfTrial+angleDiffFromMatch|subjCode)+(1|targetLabel),data=subset(d, listChoice==1),family=binomial,glmerControl(optimizer="bobyqa"))
pX <- expand.grid(angleDiffFromMatch=seq(0,22.5,by=0.1),hfTrial=c(-0.5,0.5))
predictions <- predictSE(m,pX,re.form=NA, type="response")
pX$fit <- predictions$fit
pX$se.fit <- predictions$se.fit
### Three different plotting designs - same effect ###
# Plot 1
q <- ggplot(subset(d, trialType=="test"&!is.na(matchChoice)),aes(angleDiffFromMatch,matchChoice,color=as.character(hfTrial)))+
geom_jitter(width=0.5,height=0.03,alpha=0.2)+
#geom_violinh(aes(y=as.factor(matchChoice),fill=as.character(hfTrial)),scale="count",width=0.3,alpha=0.3,color=NA)+
#geom_violinhalf(aes(y=as.factor(matchChoice),fill=as.character(hfTrial)),scale="count",width=0.3,alpha=0.3,color=NA,orientation="y")+
geom_smooth(data=pX,aes(y=fit,ymax=fit+se.fit,ymin=fit-se.fit,fill=as.character(hfTrial)),stat="identity")+
theme_classic(base_size=18)+
ylab("Probability of choosing\nnearest compass direction")+
# scale_color_brewer(palette="Set1",name="Frequency of Nearest Word",
# breaks=c(0.5,-0.5),
# labels=c("High-Frequency","Low-Frequency"),direction=-1)+
scale_color_manual(values = c("#E41A1C","#377EB8"),
name="Frequency of Compass Direction",
breaks=c(0.5,-0.5),
labels=c("High-Frequency","Low-Frequency"))+
# scale_fill_brewer(palette="Set1",name="Frequency of Nearest Compass Direction",
# breaks=c(0.5,-0.5),
# labels=c("High-Frequency","Low-Frequency"),direction=-1)+
scale_fill_manual(values = c("#E41A1C","#BDE0ED"),
name="Frequency of Compass Direction",
breaks=c(0.5,-0.5),
labels=c("High-Frequency","Low-Frequency"))+
xlab("Distance from nearest compass direction")+
geom_vline(xintercept=22.5,linetype="dashed")+
theme(legend.position=c(0.4,0.4))#+
#ylim(-0.05,1.05)
ggsave(here::here("plots","exp1_frequencyEffect_old.jpg"), width=9, height=6)
#Plot 2
p_freq1 <- ggplot(subset(d, trialType=="test"&!is.na(matchChoice)),aes(angleDiffFromMatch,as.factor(matchChoice),color=as.character(hfTrial)))+
geom_point(size = 0.5, alpha=0.3,shape=19,position = position_jitterdodge(jitter.width = 0.05,jitter.height = 0.5,
dodge.width = 0.2,
seed = 1
))+
geom_violinh(data=subset(d, trialType=="test"&!is.na(matchChoice)&hfTrial==0.5),aes(fill=as.character(hfTrial)),position = position_nudge(x = 0, y = .3 ),scale="count",width=0.4,alpha=0.5,color=NA)+
geom_violinh(data=subset(d, trialType=="test"&!is.na(matchChoice)&hfTrial==-0.5),aes(fill=as.character(hfTrial)),position = position_nudge(x = 0, y = -.3 ),scale="count",width=0.4,alpha=0.5,color=NA)+
geom_smooth(data=pX,aes(y=fit*4+1,ymax=(fit+se.fit)*4+1,ymin=(fit-se.fit)*4+1,fill=as.character(hfTrial)),stat="identity")+
theme_classic(base_size=18)+
ylab("Probability of choosing\nnearest compass direction")+
# scale_color_brewer(palette="Set1",name="Frequency of Nearest Word",
# breaks=c(0.5,-0.5),
# labels=c("High-Frequency","Low-Frequency"),direction=-1)+
scale_color_manual(values = c("#E41A1C","#377EB8"),
name="Frequency of Compass Direction",
breaks=c(0.5,-0.5),
labels=c("High-Frequency","Low-Frequency"))+
# scale_fill_brewer(palette="Set1",name="Frequency of Nearest Compass Direction",
# breaks=c(0.5,-0.5),
# labels=c("High-Frequency","Low-Frequency"),direction=-1)+
scale_fill_manual(values = c("#E41A1C","#BDE0ED"),
name="Frequency of Compass Direction",
breaks=c(0.5,-0.5),
labels=c("High-Frequency","Low-Frequency"))+
scale_y_discrete(limits=c("0","0.25","0.5","0.75","1"))+
xlab("Distance from nearest compass direction")+
geom_vline(xintercept=22.5,linetype="dashed")+
theme(legend.position=c(0.4,0.4))
p_freq1
ggsave(here::here("plots","exp1_frequencyEffect.jpg"), width=9, height=6)
# Plot 3
#alternate
p <- ggplot(subset(d, trialType=="test"&!is.na(matchChoice)),aes(angleDiffFromMatch,as.factor(matchChoice),color=as.character(hfTrial)))+
geom_point(size = 0.5, shape=19,alpha=0.2,position = position_jitterdodge(jitter.width = 0.05,jitter.height = 0.5,
dodge.width = 1.2,
seed = 1
))+
geom_violinh(data=subset(d, trialType=="test"&!is.na(matchChoice)&hfTrial==0.5),aes(fill=as.character(hfTrial)),position = position_nudge(x = 0, y = .3 ),scale="count",width=0.75,alpha=0.4, color=NA)+
geom_violinh(data=subset(d, trialType=="test"&!is.na(matchChoice)&hfTrial==-0.5),aes(fill=as.character(hfTrial)),position = position_nudge(x = 0, y = -.3 ),scale="count",width=0.75,alpha=0.4,color=NA)+
geom_smooth(data=pX,aes(y=fit*4+1,ymax=(fit+se.fit)*4+1,ymin=(fit-se.fit)*4+1,fill=as.character(hfTrial)),stat="identity")+
theme_classic(base_size=18)+
ylab("Probability of choosing\nnearest compass direction")+
# scale_color_brewer(palette="Set1",name="Frequency of Nearest Word",
# breaks=c(0.5,-0.5),
# labels=c("High-Frequency","Low-Frequency"),direction=-1)+
scale_color_manual(values = c("#E41A1C","#377EB8"),
name="Frequency of Compass Direction",
breaks=c(0.5,-0.5),
labels=c("High-Frequency","Low-Frequency"))+
# scale_fill_brewer(palette="Set1",name="Frequency of Nearest Compass Direction",
# breaks=c(0.5,-0.5),
# labels=c("High-Frequency","Low-Frequency"),direction=-1)+
scale_fill_manual(values = c("#E41A1C","#BDE0ED"),
name="Frequency of Compass Direction",
breaks=c(0.5,-0.5),
labels=c("High-Frequency","Low-Frequency"))+
scale_y_discrete(limits=c("0","0.25","0.5","0.75","1"))+
xlab("Distance from nearest compass direction")+
geom_vline(xintercept=22.5,linetype="dashed")+
theme(legend.position=c(0.4,0.4))
ggsave(here::here("plots","exp1_frequencyEffect_alternate.jpg"), width=9, height=6)
